November  2012, 6(4): 697-708. doi: 10.3934/ipi.2012.6.697

A TV Bregman iterative model of Retinex theory

1. 

Department of Mathematics, UCLA, Los Angeles, CA 90095-1555, United States, United States

Received  April 2010 Revised  October 2012 Published  November 2012

A feature of the human visual system (HVS) is color constancy, namely, the ability to determine the color under varying illumination conditions. Retinex theory, formulated by Edwin H. Land, aimed to simulate and explain how the HVS perceives color. In this paper, we establish a total variation (TV) and nonlocal TV regularized model of Retinex theory that can be solved by a fast computational approach based on Bregman iteration. We demonstrate the performance of our method by numerical results.
Citation: Wenye Ma, Stanley Osher. A TV Bregman iterative model of Retinex theory. Inverse Problems & Imaging, 2012, 6 (4) : 697-708. doi: 10.3934/ipi.2012.6.697
References:
[1]

A. Blake, Boundary conditions of lightness computation in mondrian world,, Computer Vision Graphics and Image Processing, 32 (1985), 314.  doi: 10.1016/0734-189X(85)90054-4.  Google Scholar

[2]

D. Brainard and B. Wandell, Analysis of the Retinex theory of color vision,, J. Opt. Soc. of Am. A, 3 (1986), 1651.  doi: 10.1364/JOSAA.3.001651.  Google Scholar

[3]

L. Bregman, A relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming,, USSR Comput Math and Math. Phys., 7 (1967), 200.   Google Scholar

[4]

A. Buades, B. Coll and J. M. Morel, A review of image denoising algorithms, with a new one,, Multiscale Model. Simul., 4 (2005), 490.   Google Scholar

[5]

J. Frankle and J. McCann, Method and apparatus for lightness imaging,, US Patent no. 4384336, (1983).   Google Scholar

[6]

B. Funt, F. Ciuera and J. McCann, Retinex in Matlab,, Journal of Electronic Imaging, 13 (2004), 48.  doi: 10.1117/1.1636761.  Google Scholar

[7]

G. Gilboa and S. Osher, Nonlocal operators with applications to image processing,, Multiscale Model. Simul., 7 (2008), 1005.   Google Scholar

[8]

T. Goldstein and S. Osher, The split Bregman algorithm for L1 regularized problems,, SIAM J. Imaging Sci., 2 (2009), 323.   Google Scholar

[9]

T. Goldstein, X. Bresson and S. Osher, Geometric applications of the split Bregman method: Segmentation and surface reconstruction,, J. Sci. Comput., 45 (2010), 272.  doi: 10.1007/s10915-009-9331-z.  Google Scholar

[10]

B. K. P. Horn, Determining lightness from an image,, Computer Graphics and Image Processing, 3 (1974), 277.   Google Scholar

[11]

D. J. Jobson, Z. Rahman and G. A. Woodell, A multiscale retinex for bridging the gap between color images and the human observation of scenes,, IEEE Trans. on Image Processing, 6 (1997), 965.   Google Scholar

[12]

R. Kimmel, M. Elad, D. Shaked, R. Keshet and I. Sobel, A variational framework for retinex,, Int. Journal of Computer Vision, 52 (2003), 7.  doi: 10.1023/A:1022314423998.  Google Scholar

[13]

E. H. Land and J. J. McCann, Lightness and the retinex theory,, J. Opt. Soc. Am., 61 (1971), 1.  doi: 10.1364/JOSA.61.000001.  Google Scholar

[14]

E. H. Land, The retinex theory of color vision,, Sci. Amer., 237 (1977), 108.  doi: 10.1038/scientificamerican1277-108.  Google Scholar

[15]

E. H. Land, Recent advances in the Retinex theory and some implications for cortical computations: Color vision and the natural image,, Proc. Nat. Acad. Sci. USA, 80 (1983), 5163.  doi: 10.1073/pnas.80.16.5163.  Google Scholar

[16]

E. H. Land, An alternative technique for computation of the designator in the Retinex of color vision,, Proc. Nat. Acad. Sci. USA, 83 (1986), 3078.  doi: 10.1073/pnas.83.10.3078.  Google Scholar

[17]

Y. Lei, Y. Zhou and J. Li, An investigation of retinex algorithms for image enhancement,, Jouranl of Electron, 24 (2007), 696.   Google Scholar

[18]

W. Ma, J. M. Morel, A. Chien and S. Osher, An L1-based variational model for Retinex theory and its application to medical images,, IEEE Conference on Computer Vision and Pattern Recognition, (2011), 153.   Google Scholar

[19]

J. M. Morel, A. B. Petro and C. Sbert, Fast implementation of color constancy algorithms,, Proc. SPIE, 7241 (2009).   Google Scholar

[20]

J. M. Morel, A. B. Petro and C. Sbert, A PDE formalization of the retinex theory,, IEEE Transaction on Image Processing, 19 (2010), 2825.   Google Scholar

[21]

S. Osher, M. Burger, D. Goldfarb, J. Xu and W. Yin, An iterative regularization method for total variation based image restoration,, SIAM Multiscale Model. and Simu., 4 (2005), 460.   Google Scholar

[22]

E. Provenzi, M. Fierro, A. Rizzi, L. De Carli, D. Gadia and D. Marini, Random spray retinex: A new retinex implementation to investigate the local properties of the model,, IEEE Transactions on Image Processing, 16 (2007), 162.  doi: 10.1109/TIP.2006.884946.  Google Scholar

[23]

L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms,, Physica, D(60) (1992), 259.   Google Scholar

[24]

X. Zhang, M. Burger, X. Bresson and S. Osher, Bregmanized nonlocal regularization for deconvolution and sparse reconstruction,, SIAM J. Imaging Sci., 3 (2010), 253.   Google Scholar

show all references

References:
[1]

A. Blake, Boundary conditions of lightness computation in mondrian world,, Computer Vision Graphics and Image Processing, 32 (1985), 314.  doi: 10.1016/0734-189X(85)90054-4.  Google Scholar

[2]

D. Brainard and B. Wandell, Analysis of the Retinex theory of color vision,, J. Opt. Soc. of Am. A, 3 (1986), 1651.  doi: 10.1364/JOSAA.3.001651.  Google Scholar

[3]

L. Bregman, A relaxation method of finding the common points of convex sets and its application to the solution of problems in convex programming,, USSR Comput Math and Math. Phys., 7 (1967), 200.   Google Scholar

[4]

A. Buades, B. Coll and J. M. Morel, A review of image denoising algorithms, with a new one,, Multiscale Model. Simul., 4 (2005), 490.   Google Scholar

[5]

J. Frankle and J. McCann, Method and apparatus for lightness imaging,, US Patent no. 4384336, (1983).   Google Scholar

[6]

B. Funt, F. Ciuera and J. McCann, Retinex in Matlab,, Journal of Electronic Imaging, 13 (2004), 48.  doi: 10.1117/1.1636761.  Google Scholar

[7]

G. Gilboa and S. Osher, Nonlocal operators with applications to image processing,, Multiscale Model. Simul., 7 (2008), 1005.   Google Scholar

[8]

T. Goldstein and S. Osher, The split Bregman algorithm for L1 regularized problems,, SIAM J. Imaging Sci., 2 (2009), 323.   Google Scholar

[9]

T. Goldstein, X. Bresson and S. Osher, Geometric applications of the split Bregman method: Segmentation and surface reconstruction,, J. Sci. Comput., 45 (2010), 272.  doi: 10.1007/s10915-009-9331-z.  Google Scholar

[10]

B. K. P. Horn, Determining lightness from an image,, Computer Graphics and Image Processing, 3 (1974), 277.   Google Scholar

[11]

D. J. Jobson, Z. Rahman and G. A. Woodell, A multiscale retinex for bridging the gap between color images and the human observation of scenes,, IEEE Trans. on Image Processing, 6 (1997), 965.   Google Scholar

[12]

R. Kimmel, M. Elad, D. Shaked, R. Keshet and I. Sobel, A variational framework for retinex,, Int. Journal of Computer Vision, 52 (2003), 7.  doi: 10.1023/A:1022314423998.  Google Scholar

[13]

E. H. Land and J. J. McCann, Lightness and the retinex theory,, J. Opt. Soc. Am., 61 (1971), 1.  doi: 10.1364/JOSA.61.000001.  Google Scholar

[14]

E. H. Land, The retinex theory of color vision,, Sci. Amer., 237 (1977), 108.  doi: 10.1038/scientificamerican1277-108.  Google Scholar

[15]

E. H. Land, Recent advances in the Retinex theory and some implications for cortical computations: Color vision and the natural image,, Proc. Nat. Acad. Sci. USA, 80 (1983), 5163.  doi: 10.1073/pnas.80.16.5163.  Google Scholar

[16]

E. H. Land, An alternative technique for computation of the designator in the Retinex of color vision,, Proc. Nat. Acad. Sci. USA, 83 (1986), 3078.  doi: 10.1073/pnas.83.10.3078.  Google Scholar

[17]

Y. Lei, Y. Zhou and J. Li, An investigation of retinex algorithms for image enhancement,, Jouranl of Electron, 24 (2007), 696.   Google Scholar

[18]

W. Ma, J. M. Morel, A. Chien and S. Osher, An L1-based variational model for Retinex theory and its application to medical images,, IEEE Conference on Computer Vision and Pattern Recognition, (2011), 153.   Google Scholar

[19]

J. M. Morel, A. B. Petro and C. Sbert, Fast implementation of color constancy algorithms,, Proc. SPIE, 7241 (2009).   Google Scholar

[20]

J. M. Morel, A. B. Petro and C. Sbert, A PDE formalization of the retinex theory,, IEEE Transaction on Image Processing, 19 (2010), 2825.   Google Scholar

[21]

S. Osher, M. Burger, D. Goldfarb, J. Xu and W. Yin, An iterative regularization method for total variation based image restoration,, SIAM Multiscale Model. and Simu., 4 (2005), 460.   Google Scholar

[22]

E. Provenzi, M. Fierro, A. Rizzi, L. De Carli, D. Gadia and D. Marini, Random spray retinex: A new retinex implementation to investigate the local properties of the model,, IEEE Transactions on Image Processing, 16 (2007), 162.  doi: 10.1109/TIP.2006.884946.  Google Scholar

[23]

L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms,, Physica, D(60) (1992), 259.   Google Scholar

[24]

X. Zhang, M. Burger, X. Bresson and S. Osher, Bregmanized nonlocal regularization for deconvolution and sparse reconstruction,, SIAM J. Imaging Sci., 3 (2010), 253.   Google Scholar

[1]

Lu Liu, Zhi-Feng Pang, Yuping Duan. Retinex based on exponent-type total variation scheme. Inverse Problems & Imaging, 2018, 12 (5) : 1199-1217. doi: 10.3934/ipi.2018050

[2]

Xiaoqun Zhang, Tony F. Chan. Wavelet inpainting by nonlocal total variation. Inverse Problems & Imaging, 2010, 4 (1) : 191-210. doi: 10.3934/ipi.2010.4.191

[3]

Yunmei Chen, Xianqi Li, Yuyuan Ouyang, Eduardo Pasiliao. Accelerated bregman operator splitting with backtracking. Inverse Problems & Imaging, 2017, 11 (6) : 1047-1070. doi: 10.3934/ipi.2017048

[4]

Baoli Shi, Zhi-Feng Pang, Jing Xu. Image segmentation based on the hybrid total variation model and the K-means clustering strategy. Inverse Problems & Imaging, 2016, 10 (3) : 807-828. doi: 10.3934/ipi.2016022

[5]

Victoria Martín-Márquez, Simeon Reich, Shoham Sabach. Iterative methods for approximating fixed points of Bregman nonexpansive operators. Discrete & Continuous Dynamical Systems - S, 2013, 6 (4) : 1043-1063. doi: 10.3934/dcdss.2013.6.1043

[6]

Jia Cai, Junyi Huo. Sparse generalized canonical correlation analysis via linearized Bregman method. Communications on Pure & Applied Analysis, 2020, 19 (8) : 3933-3945. doi: 10.3934/cpaa.2020173

[7]

Victoria Martín-Márquez, Simeon Reich, Shoham Sabach. Iterative methods for approximating fixed points of Bregman nonexpansive operators. Discrete & Continuous Dynamical Systems - S, 2013, 6 (4) : 1043-1063. doi: 10.3934/dcdss.2013.6.1043

[8]

Rinaldo M. Colombo, Francesca Monti. Solutions with large total variation to nonconservative hyperbolic systems. Communications on Pure & Applied Analysis, 2010, 9 (1) : 47-60. doi: 10.3934/cpaa.2010.9.47

[9]

Xianchao Xiu, Ying Yang, Wanquan Liu, Lingchen Kong, Meijuan Shang. An improved total variation regularized RPCA for moving object detection with dynamic background. Journal of Industrial & Management Optimization, 2020, 16 (4) : 1685-1698. doi: 10.3934/jimo.2019024

[10]

Yunho Kim, Paul M. Thompson, Luminita A. Vese. HARDI data denoising using vectorial total variation and logarithmic barrier. Inverse Problems & Imaging, 2010, 4 (2) : 273-310. doi: 10.3934/ipi.2010.4.273

[11]

Mujibur Rahman Chowdhury, Jun Zhang, Jing Qin, Yifei Lou. Poisson image denoising based on fractional-order total variation. Inverse Problems & Imaging, 2020, 14 (1) : 77-96. doi: 10.3934/ipi.2019064

[12]

Sören Bartels, Marijo Milicevic. Iterative finite element solution of a constrained total variation regularized model problem. Discrete & Continuous Dynamical Systems - S, 2017, 10 (6) : 1207-1232. doi: 10.3934/dcdss.2017066

[13]

J. Mead. $ \chi^2 $ test for total variation regularization parameter selection. Inverse Problems & Imaging, 2020, 14 (3) : 401-421. doi: 10.3934/ipi.2020019

[14]

Wei Wang, Ling Pi, Michael K. Ng. Saturation-Value Total Variation model for chromatic aberration correction. Inverse Problems & Imaging, 2020, 14 (4) : 733-755. doi: 10.3934/ipi.2020034

[15]

Yunhai Xiao, Junfeng Yang, Xiaoming Yuan. Alternating algorithms for total variation image reconstruction from random projections. Inverse Problems & Imaging, 2012, 6 (3) : 547-563. doi: 10.3934/ipi.2012.6.547

[16]

Juan C. Moreno, V. B. Surya Prasath, João C. Neves. Color image processing by vectorial total variation with gradient channels coupling. Inverse Problems & Imaging, 2016, 10 (2) : 461-497. doi: 10.3934/ipi.2016008

[17]

Liyan Ma, Lionel Moisan, Jian Yu, Tieyong Zeng. A stable method solving the total variation dictionary model with $L^\infty$ constraints. Inverse Problems & Imaging, 2014, 8 (2) : 507-535. doi: 10.3934/ipi.2014.8.507

[18]

Chunlin Wu, Juyong Zhang, Xue-Cheng Tai. Augmented Lagrangian method for total variation restoration with non-quadratic fidelity. Inverse Problems & Imaging, 2011, 5 (1) : 237-261. doi: 10.3934/ipi.2011.5.237

[19]

Juan Carlos De los Reyes, Estefanía Loayza-Romero. Total generalized variation regularization in data assimilation for Burgers' equation. Inverse Problems & Imaging, 2019, 13 (4) : 755-786. doi: 10.3934/ipi.2019035

[20]

Florian Krügel. Some properties of minimizers of a variational problem involving the total variation functional. Communications on Pure & Applied Analysis, 2015, 14 (1) : 341-360. doi: 10.3934/cpaa.2015.14.341

2019 Impact Factor: 1.373

Metrics

  • PDF downloads (120)
  • HTML views (0)
  • Cited by (8)

Other articles
by authors

[Back to Top]